A100 ultra-high-strength steel is the main material of aircraft landing gear and a typical difficult-tomachine aerospace material. In order to solve the problems of low machining efficiency and difficult to control surface quality in its dry high-speed milling processing, and to balance the machining efficiency and surface quality at the same time, the multi-objective optimization experimental research on milling process parameters is carried out with the material removal rate, surface roughness and its signal-to-noise ratio as the evaluation indexes. Firstly, orthogonal experiments were designed to analyze the influence law of milling process parameters on the evaluation indexes based on the experimental results, and establish the optimization target prediction model. Then, the fast non-dominated sorting genetic algorithm (NSGA-II) was used to solve the problem and get the non-dominated solution set of the process parameters. Finally, after considering the machining efficiency, the obtained non-dominated solution set is screened twice by TOPSIS comprehensive evaluation method to obtain the optimal process parameter combinations. After comparison, it is found that the optimized surface roughness is reduced by 34.52% compared to the optimal value before optimization, while the signal-to-noise ratio is improved by 7.14%, which provides a reference basis for the reasonable selection of dry highspeed milling parameters.